{
  "cells": [
    {
      "cell_type": "code",
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "# SVM: Maximum margin separating hyperplane\n",
        "\n",
        "Plot the maximum margin separating hyperplane within a two-class\n",
        "separable dataset using a Support Vector Machine classifier with\n",
        "linear kernel.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Automatically created module for IPython interactive environment\n"
          ]
        },
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "print(__doc__)\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm\n",
        "from sklearn.datasets import make_blobs\n",
        "\n",
        "\n",
        "# we create 40 separable points\n",
        "X, y = make_blobs(n_samples=40, centers=2, random_state=6)\n",
        "\n",
        "# fit the model, don't regularize for illustration purposes\n",
        "clf = svm.SVC(kernel='linear', C=1000)\n",
        "clf.fit(X, y)\n",
        "\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n",
        "\n",
        "# plot the decision function\n",
        "ax = plt.gca()\n",
        "xlim = ax.get_xlim()\n",
        "ylim = ax.get_ylim()\n",
        "\n",
        "# create grid to evaluate model\n",
        "xx = np.linspace(xlim[0], xlim[1], 30)\n",
        "yy = np.linspace(ylim[0], ylim[1], 30)\n",
        "YY, XX = np.meshgrid(yy, xx)\n",
        "xy = np.vstack([XX.ravel(), YY.ravel()]).T\n",
        "Z = clf.decision_function(xy).reshape(XX.shape)\n",
        "\n",
        "# plot decision boundary and margins\n",
        "ax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,\n",
        "           linestyles=['--', '-', '--'])\n",
        "# plot support vectors\n",
        "ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,\n",
        "           linewidth=1, facecolors='none', edgecolors='k')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
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